Quantitative Researcher — High-Frequency Trading + PnL Share

Sryantra Capital; New York, Northern (NY, KY); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

PnL share

Proprietary datasets

Collaborative culture

Health/dental/vision coverage

Job summary

Sryantra Capital in New York seeks a quantitative researcher to design and backtest high-frequency trading signals using rigorous statistics and machine learning on massive datasets.

You will mine large-scale market data for predictive features and structural inefficiencies, research market microstructure, and collaborate with traders and developers to ship production-ready strategies while continuously adapting to changing markets.

Qualifications

• PhD or Master’s in a quantitative field (Mathematics, Physics, Computer Science, Statistics, or related).
• Strong programming skills in C++, Python, or Rust.
• Deep grounding in probability, statistics, and time-series analysis.
• Experience applying ML frameworks (PyTorch, TensorFlow, scikit-learn) to financial data.
• Curiosity, creativity, and a rigorous scientific approach.
• Fluency in English is required.

Responsibilities

• Design and backtest high-frequency trading signals using statistical and machine-learning methods.
• Mine large-scale market data for predictive features and structural inefficiencies.
• Research market microstructure and exchange dynamics with rigour.
• Improve portfolio construction and execution to reduce slippage and lift returns.
• Partner with traders and developers to ship production-ready strategies.
• Continuously monitor and adapt live strategies as markets change.

Skills

C++

Python

Rust

Time-series analysis

Statistics

Machine Learning

English fluency

Education

PhD or Master’s in a quantitative field

Tools

PyTorch

TensorFlow

scikit-learn

Job description

Sryantra Capital in New York seeks a quantitative researcher to design and backtest high-frequency trading signals using rigorous statistics and machine learning on massive datasets.

You will mine large-scale market data for predictive features and structural inefficiencies, research market microstructure, and collaborate with traders and developers to ship production-ready strategies while continuously adapting to changing markets.